基于遗传神经网络的机器人视觉控制方法

Method of robot vision based on genetic neural network

传统的机器人伺服控制需要计算雅克比矩阵以及解雅克比矩阵的逆才能实现系统设计,计算量很大且困难、系统结构复杂。设计了一种基于遗传神经网络的六关节机器人视觉伺服系统,并利用遗传算法对神经网络进行优化。此方法不但解决了系统求解雅克比矩阵及其逆计算量大的问题,而且不需要对摄像机内部参数和机器人参数进行标定,同时由于遗传算法的加入,提高了神经网络的性能,不仅大大简化了控制系统,提高了系统的速度,同时也保证了控制系统的精度。

Traditional robot visual servoing control system to realize the calculation of the Jacobian matrix and the solution of the inverse Jacobian matrix,a large amount of calculation,the complex structure of the system,and realize the difficult calculation.In this paper,a six joint robot visual servoing system based on genetic neural network is designed,and genetic algorithm is used to optimize the neural network.This method not only solves the system of solving the Jacobian matrix and its inverse computation problem,and does not require the camera internal parameters and robot parameters are determined at the same time due to the addition of the genetic algorithm,to improve the performance of neural network,not only greatly simplifies the control system,improve the system speed,but also to ensure the control system precision.